Integrated production simulator based on capacitance-resistance model

ABSTRACT

A well-based production simulator is provided, which predicts the quantity of fluids produced per phase, per well and per time as a function of operational field parameters. The invention combines a petroleum reservoir simulator with a petroleum production facility simulator to obtain an integrated model to quickly and accurately forecast production on a well-by-well basis. The efficiency of the petroleum reservoir simulator is derived from its unique formulation, which solves for the production well&#39;s flow rate rather than the petroleum reservoir pressure. The simulator properly represents viscous, capillary and gravity forces, as well as complex fluid descriptions, including three-phase black-oil formulations.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Prov. App. No. 62/002,470,filed May 23, 2014, and entitled “INTEGRATED PRODUCTION SIMULATOR BASEDON CAPACITANCE-RESISTANCE MODEL,” the disclosure of which isincorporated herein in its entirety.

BACKGROUND

Hydrocarbon reservoirs are exploited by drilling wells in a hydrocarbonbearing geologic formation. Both producing wells and injecting wells aretypically used. The role of producing wells (producers) is to allowhydrocarbons to flow to the surface. Injecting wells (injectors) aredrilled in order to maintain the reservoir pressure by injecting fluids(typically water or gas) to replace the produced fluids.

The key to a successful exploitation operation of a petroleum reservoiris to efficiently design and operate wells. In order to guide andoptimize well operations, simulators are often used. The role ofreservoir simulators is to forecast the production of wells in order toevaluate the possible outcomes of operational changes.

Reservoir simulators can be created in a variety of ways, but for thepurpose of production optimization, it is desirable to take an approachthat is both fast and accurate. The accuracy of the simulator is definedas the predictive power of the simulator: its ability to predict futurewell performance accurately and with a high level of confidence. Thesimulator's accuracy helps guarantee the economic success of theoperational changes implemented. The speed of the simulator is definedas the time it takes to create or update a model and to perform asimulation. A fast simulator is desirable to update the model with newdata in order to support daily operational decisions in a timelyfashion.

The standard approach followed in the petroleum industry to modelreservoirs is to use grid-based reservoir simulators. These simulatorsoften rely on a finite volume discretization of the equations governingthe motion of reservoir fluids. Alternate discretization methods, suchas finite element methods, are also used from time to time. Thesemethods all have in common that the primary unknowns solved during thecomputation are the fluid pressures of each fluid phase and thecomposition of each fluid component.

Classical grid-based reservoir simulation can be very accurate but isusually prohibitively slow. These models are large and requiresignificant computer resources to run them. They are prohibitively slowfor use in supporting day-to-day decisions related to productionoptimization. Grid-based reservoir simulation models are used primarilyto support long-term field development decisions, such as the additionof new wells or changes to the exploitation strategy of the field.

In recent years, a new type of reservoir simulation method has beendeveloped in order to offer a faster alternative to classical grid-basedreservoir simulators. This new class of methods, coined“Capacitance-Resistance” (“CR”) models in the literature, does notdepend on solving the fluid pressures and compositions on a staticgeometric grid representing the reservoir geology.

The fundamental difference between CR models and classical reservoirsimulation models is that CR models rely on a reformulation of theequation governing the flow of fluids in porous media. Where classicalreservoir simulation models are designed to find the fluid pressure andcompositions within the reservoir, CR models directly solve for the wellproduction rates of each fluid, without having to solve for the fluidpressure and compositions.

Although much faster than classical reservoir simulation models, currentCR models are limited in their application as they currently rely on asignificant simplification of the reservoir flow equations. Criticallimitations include neglecting the effect of fluid flow in theproduction or injection wellbore and surface facilities. At thereservoir level, these models neglect the effects of fluidcompressibility, as well as capillary and gravitational forces. Currentformulations also rely on a simplified description of the fluid system,involving only two fluid phases.

BRIEF SUMMARY

Embodiments described herein are directed to modeling a productionsystem and generating a production forecast for individual wells. In oneembodiment, a computer system accesses portions of first productionsystem information from a capacitance-resistance model of the productionsystem, where the production system corresponds to a productionreservoir. The computer system further accesses portions of secondproduction system information from a well-bore model, a flow line and/ora production facility. The computer system then generates an integratedproduction simulator using both the first and second accessed productionsystems information, and implements the integrated production simulatorto determine the quantity of fluids produced per phase over time as afunction of operational field parameters corresponding to the productionsystem by identifying the flow rate for the production reservoir.

In another embodiment, an integrated well-based production simulatorsystem is provided. The integrated well-based production simulatorsystem includes: a capacitance-resistance (CR) simulator configured torepresent the flow of fluids in a production reservoir, a wellboresimulator configured to represent the flow of fluids in a wellbore, asurface facility simulator configured to represent the flow of fluidsthrough at least one of the following surface facilities: pipelines, aproduction gathering facility, a separation facility, and an injectiondistribution facility, where the integrated well-based productionsimulator is configured to provide a system-wide representation of fluidflow through the production reservoir, the wellbore and at least onesurface facility.

In yet another embodiment, a computer system generates a productionforecast for individual wells. The computer system accesses operationalparameters for a well and provides an integrated well-based productionsimulator by solving a specified system of equations. The integratedwell-based production simulator then generates a production forecast forthe well using the operational parameters.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be apparent to one of ordinary skill inthe art from the description, or may be learned by the practice of theteachings herein. Features and advantages of embodiments describedherein may be realized and obtained by means of the instruments andcombinations particularly pointed out in the appended claims. Featuresof the embodiments described herein will become more fully apparent fromthe following description and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

To further clarify the above and other features of the embodimentsdescribed herein, a more particular description will be rendered byreference to the appended drawings. It is appreciated that thesedrawings depict only examples of the embodiments described herein andare therefore not to be considered limiting of its scope. Theembodiments will be described and explained with additional specificityand detail through the use of the accompanying drawings in which:

FIG. 1 illustrates a computer-implemented or computer-controlledarchitecture that can be used to gather, analyze and/or display datagathered from and about a reservoir.

FIG. 2 illustrates an example schematic of a production and injectionsystem of a petroleum field.

FIG. 3 illustrates a computer architecture in which embodimentsdescribed herein may operate including modeling a production system

FIG. 4 illustrates a flowchart of an example method for modeling aproduction system.

FIG. 5 illustrates a flowchart of an example method for generating aproduction forecast for individual wells.

FIG. 6 illustrates an embodiment of an integrated well-based productionsimulator system.

DETAILED DESCRIPTION

Embodiments described herein are directed to modeling a productionsystem and to generating a production forecast for individual wells. Inone embodiment, a computer system accesses portions of first productionsystem information from a capacitance-resistance model of the productionsystem, where the production system corresponds to a productionreservoir. The computer system further accesses portions of secondproduction system information from a well-bore model, a flow line and/ora production facility. The computer system then generates an integratedproduction simulator using both the first and second accessed productionsystems information, and implements the integrated production simulatorto determine the quantity of fluids produced per phase over time as afunction of operational field parameters corresponding to the productionsystem by identifying the flow rate for the production reservoir.

In another embodiment, an integrated well-based production simulatorsystem is provided. The integrated well-based production simulatorsystem includes: a capacitance-resistance (CR) simulator configured torepresent the flow of fluids in a production reservoir, a wellboresimulator configured to represent the flow of fluids in a wellbore, asurface facility simulator configured to represent the flow of fluidsthrough at least one of the following surface facilities: pipelines, aproduction gathering facility, a separation facility, and an injectiondistribution facility, where the integrated well-based productionsimulator is configured to provide a system-wide representation of fluidflow through the production reservoir, the wellbore and at least onesurface facility.

In yet another embodiment, a computer system generates a productionforecast for individual wells. The computer system accesses operationalparameters for a well and provides an integrated well-based productionsimulator by solving a specified system of equations. The integratedwell-based production simulator then generates a production forecast forthe well using the operational parameters.

The following discussion now refers to a number of methods and methodacts that may be performed. It should be noted that, although the methodacts may be discussed in a certain order or illustrated in a flow chartas occurring in a particular order, no particular ordering isnecessarily required unless specifically stated, or required because anact is dependent on another act being completed prior to the act beingperformed.

Embodiments described herein may implement various types of computingsystems. These computing systems are now increasingly taking a widevariety of forms. Computing systems may, for example, be handhelddevices, appliances, laptop computers, desktop computers, mainframes,distributed computing systems, or even devices that have notconventionally been considered a computing system. In this descriptionand in the claims, the term “computing system” is defined broadly asincluding any device or system (or combination thereof) that includes atleast one physical and tangible processor, and a physical and tangiblememory capable of having thereon computer-executable instructions thatmay be executed by the processor. A computing system may be distributedover a network environment and may include multiple constituentcomputing systems.

Computing systems (e.g. 102 in FIG. 1) typically include at least oneprocessing unit and memory. The memory may be physical system memory,which may be volatile, non-volatile, or some combination of the two. Theterm “memory” may also be used herein to refer to non-volatile massstorage such as physical storage media. If the computing system isdistributed, the processing, memory and/or storage capability may bedistributed as well.

As used herein, the term “executable module” or “executable component”can refer to software objects, routings, or methods that may be executedon the computing system. The different components, modules, engines, andservices described herein may be implemented as objects or processesthat execute on the computing system (e.g., as separate threads).

In the description that follows, embodiments are described withreference to acts that are performed by one or more computing systems.If such acts are implemented in software, one or more processors of theassociated computing system that performs the act direct the operationof the computing system in response to having executedcomputer-executable instructions. For example, such computer-executableinstructions may be embodied on one or more computer-readable media thatform a computer program product. An example of such an operationinvolves the manipulation of data. The computer-executable instructions(and the manipulated data) may be stored in the memory of the computingsystem 102. Computing systems may also contain communication channelsthat allow the computing system to communicate with other messageprocessors over a wired or wireless network.

Embodiments described herein may comprise or utilize a special-purposeor general-purpose computer system that includes computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. The system memory may be included within theoverall memory. The system memory may also be referred to as “mainmemory”, and includes memory locations that are addressable by the atleast one processing unit over a memory bus in which case the addresslocation is asserted on the memory bus itself. System memory has beentraditionally volatile, but the principles described herein also applyin circumstances in which the system memory is partially, or even fully,non-volatile.

Embodiments within the scope of the present invention also includephysical and other computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general-purpose or special-purpose computer system.Computer-readable media that store computer-executable instructionsand/or data structures are computer storage media. Computer-readablemedia that carry computer-executable instructions and/or data structuresare transmission media. Thus, by way of example, and not limitation,embodiments of the invention can comprise at least two distinctlydifferent kinds of computer-readable media: computer storage media andtransmission media.

Computer storage media are physical hardware storage media that storecomputer-executable instructions and/or data structures. Physicalhardware storage media include computer hardware, such as RAM, ROM,EEPROM, solid state drives (“SSDs”), flash memory, phase-change memory(“PCM”), optical disk storage, magnetic disk storage or other magneticstorage devices, or any other hardware storage device(s) which can beused to store program code in the form of computer-executableinstructions or data structures, which can be accessed and executed by ageneral-purpose or special-purpose computer system to implement thedisclosed functionality of the invention.

Transmission media can include a network and/or data links which can beused to carry program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system. A “network” isdefined as one or more data links that enable the transport ofelectronic data between computer systems and/or modules and/or otherelectronic devices. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computersystem, the computer system may view the connection as transmissionmedia. Combinations of the above should also be included within thescope of computer-readable media.

Further, upon reaching various computer system components, program codein the form of computer-executable instructions or data structures canbe transferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a “NIC”), and theneventually transferred to computer system RAM and/or to less volatilecomputer storage media at a computer system. Thus, it should beunderstood that computer storage media can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at one or more processors, cause ageneral-purpose computer system, special-purpose computer system, orspecial-purpose processing device to perform a certain function or groupof functions. Computer-executable instructions may be, for example,binaries, intermediate format instructions such as assembly language, oreven source code.

Those skilled in the art will appreciate that the principles describedherein may be practiced in network computing environments with manytypes of computer system configurations, including, personal computers,desktop computers, laptop computers, message processors, hand-helddevices, multi-processor systems, microprocessor-based or programmableconsumer electronics, network PCs, minicomputers, mainframe computers,mobile telephones, PDAs, tablets, pagers, routers, switches, and thelike. The invention may also be practiced in distributed systemenvironments where local and remote computer systems, which are linked(either by hardwired data links, wireless data links, or by acombination of hardwired and wireless data links) through a network,both perform tasks. As such, in a distributed system environment, acomputer system may include a plurality of constituent computer systems.In a distributed system environment, program modules may be located inboth local and remote memory storage devices.

Those skilled in the art will also appreciate that the invention may bepracticed in a cloud computing environment. Cloud computing environmentsmay be distributed, although this is not required. When distributed,cloud computing environments may be distributed internationally withinan organization and/or have components possessed across multipleorganizations. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services). The definition of “cloudcomputing” is not limited to any of the other numerous advantages thatcan be obtained from such a model when properly deployed.

Still further, system architectures described herein can include aplurality of independent components that each contribute to thefunctionality of the system as a whole. This modularity allows forincreased flexibility when approaching issues of platform scalabilityand, to this end, provides a variety of advantages. System complexityand growth can be managed more easily through the use of smaller-scaleparts with limited functional scope. Platform fault tolerance isenhanced through the use of these loosely coupled modules. Individualcomponents can be grown incrementally as business needs dictate. Modulardevelopment also translates to decreased time to market for newfunctionality. New functionality can be added or subtracted withoutimpacting the core system.

FIG. 1 illustrates a computing architecture in which acomputer-implemented monitoring system 100 may operate. Thecomputer-implemented monitoring system 100 may be configured to monitorreservoir performance, analyze information regarding reservoirperformance, display dashboard metrics, and optionally provide forcomputer-controlled modifications to maintain optimal oil wellperformance. Monitoring system 100 may include a main data gatheringcomputer system 102 comprised of one or more computers (potentiallylocated near a reservoir) which are linked to reservoir sensors 104.Each of these computers typically includes at least one processor andsystem memory. Computer system 102 may comprise a plurality of networkedcomputers (e.g., each of which is designed to analyze a subset of theoverall data generated by and received from the sensors 104).

Reservoir sensors 104 are typically positioned at different locationswithin a producing oil well, and may include both surface andsub-surface sensors. Sensors 104 may also be positioned at waterinjection wells, observation wells, etc. The data gathered by thesensors 104 can be used to generate performance metrics (e.g., leadingand lagging indicators of production and recovery). The computer system102 may therefore include a data analysis module 106 programmed togenerate metrics from the received sensor data. A user interface 108provides interactivity with a user, including the ability to input datarelating to a real displacement efficiency, vertical displacementefficiency, and pore displacement efficiency. Data storage device 110can be used for long term storage of data and metrics generated from thedata.

According to one embodiment, the computer system 102 can provide for atleast one of manual or automatic adjustment to production 112 byreservoir production units 114 (e.g., producing oil wells, waterinjection wells, gas injection wells, heat injectors, and the like, andsub-components thereof). Adjustments might include, for example changesin volume, pressure, temperature, well bore path (e.g., via closing oropening of well bore branches). The user interface 108 permits manualadjustments to production 112. The computer system 102 may, in addition,include alarm levels or triggers that, when certain conditions are met,provide for automatic adjustments to production 112.

Monitoring system 100 may also include one or more remote computers 120that permit a user, team of users, or multiple parties to accessinformation generated by main computer system 102. For example, eachremote computer 120 may include a dashboard display module 122 thatrenders and displays dashboards, metrics, or other information relatingto reservoir production. Each remote computer 120 may also include auser interface 124 that permits a user to make adjustment to production112 by reservoir production units 114. Each remote computer 120 may alsoinclude a data storage device (not shown).

Individual computer systems within monitoring system 100 (e.g., maincomputer system 102 and remove computers 120) can be connected to anetwork 130, such as, for example, a local area network (“LAN”), a widearea network (“WAN”), or even the Internet. The various components canreceive and send data to each other, as well as other componentsconnected to the network. Networked computer systems (i.e. cloudcomputing systems) and computers themselves constitute a “computersystem” for purposes of this disclosure.

Networks facilitating communication between computer systems and otherelectronic devices can utilize any of a wide range of (potentiallyinteroperating) protocols including, but not limited to, the IEEE 802suite of wireless protocols, Radio Frequency Identification (“RFID”)protocols, ultrasound protocols, infrared protocols, cellular protocols,one-way and two-way wireless paging protocols, Global Positioning System(“GPS”) protocols, wired and wireless broadband protocols,ultra-wideband “mesh” protocols, etc. Accordingly, computer systems andother devices can create message related data and exchange messagerelated data (e.g., Internet Protocol (“IP”) datagrams and other higherlayer protocols that utilize IP datagrams, such as, Transmission ControlProtocol (“TCP”), Remote Desktop Protocol (“RDP”), Hypertext TransferProtocol (“HTTP”), Simple Mail Transfer Protocol (“SMTP”), Simple ObjectAccess Protocol (“SOAP”), etc.) over the network.

Computer systems and electronic devices may be configured to utilizeprotocols that are appropriate based on corresponding computer systemand electronic device on functionality. Components within thearchitecture can be configured to convert between various protocols tofacilitate compatible communication. Computer systems and electronicdevices may be configured with multiple protocols and use differentprotocols to implement different functionality. For example, a sensor104 at an oil well might transmit data via wire connection, infrared orother wireless protocol to a receiver (not shown) interfaced with acomputer, which can then forward the data via fast Ethernet to maincomputer system 102 for processing. Similarly, the reservoir productionunits 114 can be connected to main computer system 102 and/or remotecomputers 120 by wire connection or wireless protocol.

As indicated above, a capacitance-resistance model (or CR model) may beused to characterize the connectivity between injection and productionwells and can determine an injection scheme that maximizes the value ofthe reservoir asset. CR model parameters are identified using linear andnonlinear regression. The CR model is then used together with anonlinear optimization algorithm to compute a set of future injectionrates which maximize discounted net profit. CR models solve forproduction rates of each fluid without solving for fluid pressure andcompositions (as in grid-based). CR models, however, neglect fluid flowin production facilities and wellbores and neglect the effects of fluidcompressibility and capillary and gravitational forces, and are limitedto two fluid phases.

Embodiments described herein include a reservoir simulator accurateenough to generate a reliable forecast of individual wells as a functionof operational parameters and fast enough to be used in practice todrive the operational decisions required to optimize the exploitation ofpetroleum reservoirs. The speed of the reservoir simulator is due tomultiple factors including a differentiated formulation and solutionworkflow.

FIG. 2 illustrates a schematic of a production and injection system of apetroleum field. The production wells 203 allow reservoir fluids (fromreservoir 208) to flow through their completion 204 and to the surface,where a network of pipelines (e.g. production tubing 205) carry thefluids to production gathering facilities 202, and in turn, to aseparator 201. The separator system isolates each fluid phase (typicallyoil, gas and water). In some cases, the water or gas produced andseparated are then sent to an injection distribution system 206. Theinjection distribution system can also receive injection fluids fromexterior sources. The injection wells 207 receive the fluids to beinjected from the injection distribution system 206 via a network ofpipelines and inject these fluids in the petroleum reservoirs throughwell completions 204.

In some embodiments, reservoir fluid mixtures may be composed of two ormore phases. For example, two phases may be considered in the followingderivation, where the oil phase is designated with the subscript “o” andthe water phase with the subscript “w”. Embodiments may be extended tomore complex fluid compositions including three or more fluid phases. Inthis example, a producing well is located in a hydrocarbon reservoir.Naming V the drainage volume of the well, the mass balance equation overthe water and oil fluid components written over the drainage volume ofthe well can be expressed as:

$\begin{matrix}{{{\frac{S_{w}}{t} + {{S_{w}( {c_{w} + c_{f}} )}\frac{p}{t}} + \frac{q_{w} - i_{w}}{V}} = 0},{and}} & ( {{Eq}.\mspace{14mu} 1} ) \\{{\frac{S_{o}}{t} + {{S_{o}( {c_{o} + c_{f}} )}\frac{p}{t}} + \frac{q_{o}}{V}} = 0.} & ( {{Eq}.\mspace{14mu} 2} )\end{matrix}$

In Eq. 1 and 2, t designates a time variable. The unknowns of theequations are p, the fluid pressure, as well as S_(w) and S_(o), thewater and oil saturations. c_(w), c_(o) and c_(f) are respectively, thewater, oil and rock formation compressibility. q_(o) and q_(w) are theoil and water production rates of the well of interest and i_(w) is thewater injection rate received by the drainage volume of the well. Toclose the system, the fundamental property of the oil and watersaturation is used:

S _(w) +S _(o)=1.   (Eq. 3)

In some cases, reservoir simulators may be configured to directly solvethe system formed by Eq. 1 and 2 using a numerical discretization methodon a grid describing the reservoir geometry and rock properties. Variousapproaches may be used including finite difference, finite volume andfinite element methods. Simulators typically solve simultaneously thepressure and saturation unknowns, using a scheme referred to as afully-implicit scheme. Some simulators solve the pressure and saturationunknowns sequentially.

Summing Eq. 1 and 2, and using Eq. 3 to simplify the saturationderivatives, the pressure equation may be obtained, describing the flowproblem:

$\begin{matrix}{{{c_{t}V\frac{p}{t}} + q_{t} - i_{w}} = 0} & ( {{Eq}.\mspace{14mu} 4} )\end{matrix}$

where the total production rate q_(t)=q_(o)+q_(w) is defined along withthe total compressibility c_(t)=(S_(o)c_(o)+S_(w)c_(w)+c_(f)).

Some reservoir simulators may rely on Eq. 4 to solve for the fluidpressure throughout the reservoir, and then solve either Eq. 1 or Eq. 2in order to obtain the fluid saturations. This solution approach iscalled the implicit-pressure-explicit-saturation approach and is knownto speed up simulation runtime as it allows the numerical discretizationalgorithms to be tailored to the mathematical character of eachequation. The pressure equation of Eq. 4 describes the flow problem,which is near-parabolic in nature, the saturation equations of Eq. 1 and2 describe the transport problem, which is near-hyperbolic in nature.

Introducing the productivity index J of the producing well, the equationlinking the total production rate to the reservoir pressure reads:

q _(t) =j(p−p _(BH))   (Eq. 5)

where p_(BH) designates the bottom-hole pressure of the producing well.

By differentiating Eq. 5 with respect to time and replacing thereservoir pressure derivative term in Eq. 4, an equation may be obtainedthat depends solely on the pressure and rate of the producing well:

$\begin{matrix}{{{\tau \frac{q_{t}}{t}} + q_{t}} = {i_{w} - {c_{t}V\frac{p_{BH}}{{t}\;}}}} & ( {{Eq}.\mspace{14mu} 6} )\end{matrix}$

where

$\tau = \frac{c_{t}V}{J}$

is defined as a time constant.

The CR models solve a system of equation composed of a form of Eq. 6 andeither Eq. 1 or Eq. 2. The fundamental difference between CR models andclassical reservoir models is that Eq. 6 does not involve the reservoirpressure. Instead, the production rate of the well is determineddirectly. This difference provides a speed advantage for CR models overtraditional methods.

Embodiments described herein are designed to integrate the productionsystem 202 and the injection system 206. CR models are typically usedsolely as reservoir simulators. The fundamental unknowns of CR modelsare the production rates of each fluid component. The well bottom-holepressure is usually seen as a constraint on the producing well. Inreality, the bottom-hole pressure is often an unknown just as much asthe production rates. The effective well constraint of a well could belocated in a variety of upstream locations including, but not limitedto, the tubing-head pressure, the flow-line pressure, the manifoldpressure or separator pressure. Each of these locations may beconstraints on a well's production.

To more completely model the production system, embodiments hereinimplement CR models in conjunction with models of the wellbore, surfaceflow-lines and associated production facilities to create an integratedproduction model. To do so, the bottom-hole pressure of Eq. 6 is viewedas an unknown rather than a constraint and additional equations areintroduced to represent the dependence of the bottom-hole pressure onthe architecture of the production system. This approach allows the CRmodel to be constrained by the actual control mechanisms in the field,such as well-head choke size or artificial lift parameters.

Several possible levels of control are possible including the tubinghead pressure, p_(TH), and the flow-line pressure, p_(FL), located atthe well-head upstream and downstream of the choke, respectively.Further downstream, pressures at other flow-lines, manifolds orseparators could also be used. These are labeled p_(DS) to designate ageneral downstream pressure.

Relating the pressures along the production facility system, from thewellbore to the separator is an exercise often performed by Petroleum orChemical Engineers. A variety of approaches may be used depending on thesystem components and flow conditions. Any approach will result in amodeling of the system that will link a downstream pressure, p_(DS) tothe bottom-hole pressure, p_(BH) and flow rates of each fluid component;here, q_(o) and q_(w) are considered for completeness, but the approachis not limited to such relatively simplistic systems. The productionsystem may be modeled through a function f_(p) _(c1) _(, . . . , p)_(cm) ^(facility), such that:

p _(BH) =f _(p) _(c1) _(, . . . , p) _(cm) ^(facility)(p _(DS) , q ₀ , q_(w))   (Eq. 7)

where p_(c1), . . . , p_(cm) are m independent control parameters on thesystem.

Combining Eq. 7 with the previous system composed of Eq. 5 and Eq. 1 orEq. 2 leads to an integrated well-based production simulator (e.g. asshown in FIG. 6).

In different embodiments of the method, f_(p) _(c1) _(, . . . , p) _(cm)^(facility) can take various forms. If the dynamics of the flow in theproduction system are simple enough, the function can be an analyticalformula, explicitly expressing the dependence of the bottom-holepressure to the downstream pressure, flow rates and operationalparameters. In other, more complex cases, a numerical model of the flowequations may be used, so that the function will be embodied as asimulator of wellbore flow dynamics and/or a surface facility simulator.In one embodiment, the function can take the form of a table ofpre-computed solutions. Such vertical lift tables may be used torepresent the well flow dynamics in reservoir simulation software. Theseconcepts will be explained further below with regard to methods 400 and500 of FIGS. 4 and 5, respectively.

In view of the systems and architectures described above, methodologiesthat may be implemented in accordance with the disclosed subject matterwill be better appreciated with reference to the flow charts of FIGS. 4and 5. For purposes of simplicity of explanation, the methodologies areshown and described as a series of blocks. However, it should beunderstood and appreciated that the claimed subject matter is notlimited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methodologies described hereinafter.

FIG. 4 illustrates a flowchart of a method 400 for modeling a productionsystem. The method 400 will now be described with frequent reference tothe components and data elements shown in FIGS. 1, 2 and 3,respectively.

Method 400 includes accessing one or more portions of first productionsystem information from a capacitance-resistance model of the productionsystem, the production system corresponding to at least one productionreservoir (410). For example, accessing module 307 of computer system301 may be configured to access first production system information 315received from production reservoir 314. The first production systeminformation 315 may be used by the capacitance-resistance (CR) model 308to model a production system 320. The production system 320 may include,for example, production reservoir 314, well-bore model 317, flow lines318 and/or a production facility 319. Each of these production systemcomponents may cause constraints on the production of fluids such aspetroleum.

As such, the CR model 308 takes into account production systeminformation for the production system 320 as a whole and generatesproduction forecasts 313 based on simulations provided by the model.Each of the modules of computer system 301 may interact with or beprocessed by processor 302 and/or memory 303. Moreover, communicationswith other computing systems or users may occur using the communicationsmodule 304. For example, first and second production system information315 and 316 may be received from other computing systems. Similarly,user 305 may interact with the computer system 301 using input 306,which is received at the communications module 304.

Method 400 further includes accessing one or more portions of secondproduction system information from at least one of a well-bore model, aflow line and a production facility (420). As indicated above, aproduction system (e.g. 320) includes multiple different elements,including a production reservoir 314, a well-bore 317, a flow line 318and a production facility 319, among others. The second productionsystem information 316 may be sent from any of these elements includinga well-bore model 317 (or directly from well-bore datasets), from flowline data 318 or from production facility data 319. Each of these partsof the production system 320 may introduce different pressures or forceson the production material (oil, water, gas, etc.). And, as such, eachpart may introduce constraints on the production system, reducing orincreasing efficiency in some manner.

An integrated production simulator 310 may be configured to take bothfirst production system information 315 and second production systeminformation 316 into account when generating a production forecast 313for a production system. The integrated production simulator may begenerated using both the first and second accessed production systeminformation (430). The simulator generating module 309 of computersystem 101, for example, may generate integrated production simulator310, which may be used to determine the quantity of fluids produced perphase over time as a function of operational field parameterscorresponding to the production system by identifying the flow rate forthe production reservoir (440). Thus, the simulator 310 may not only beused to generate production forecasts, but also to determine thequantity of fluids 311 produced per phase (e.g. separately for oil, gasand water, or other phases). The simulator determines the quantity offluids produced per phase over time as a function of operational fieldparameters corresponding to the production system by identifying theflow rate 312 for the production reservoir. The operational fieldparameters may include well-head choke size, artificial lift parametersor other field parameters. In this manner, the integrated productionsimulator 310 is constrained by actual control mechanisms used in aproduction system.

In some embodiments, the determined quantity of fluids produced perphase over time as a function operational field parameters correspondingto the production system 320 may be further analyzed by computer system301 to determine whether additional operations are to be initiated onthe production system. In some cases, the further analysis may indicatethat a certain flow line is reducing fluid flow, or a certain well-boreor portion of a production facility is reducing efficiency in producingmaterial. Accordingly, in such cases, additional operations may beundertaken to increase fluid flow in the determined flow line 318. Otheroperations may be initiated to increase efficiency in the well bore 317or the determined portion(s) of the production facility 319.

If it is determined that additional operations are to be initiated onone or more components the production system 320, the computer system301 may analyze the determined quantity of fluids produced per phaseover time as a function of the operational field parameterscorresponding to the production system to determine the degree to whichthe additional operations are to be performed. Accordingly, if fluidflow is drastically reduced at a particular component, the computersystem 301 may indicate that operations are to be taken immediately inrelation to that component to a degree sufficient to counteract thereduction in fluid flow.

The integrated production simulator 310 may thus simulate and show whereconstraints exist across each piece of the production system 320. Theintegrated production simulator 310 is configured to show the flow ofpetroleum and aqueous fluids from the production reservoir through theproduction facility. In some cases, identifying the flow rate for theproduction reservoir includes identifying a production rate for one ormore fluids in the production reservoir. These fluids may include water,gas, oil or other fluids. The integrated production simulator 310 may beconfigured to account for fluid flow in both the well-bore and theproduction facility. The integrated production simulator 310 may furtherbe configured to account for fluid compressibility, capillary forcesand/or gravitational forces. As such, the integrated productionsimulator 310 may provide a more complete and more thorough indicationof operating conditions of a particular production system.Determinations of fluid compressibility, capillary forces andgravitational forces act to remove variables in the simulations, andthus provide a more accurate production forecast 313 or indication ofthe quantity of fluids 311 produced per phase over time.

In some cases, the integrated production simulator 310 is configured tomodel the bottom-hole's dependence on one or more components ofproduction system architecture (e.g. components 314, 317, 318 or 319).The bottom-hole pressure for the production system 320 may be identifiedas an unknown element, as opposed to being identified as a constraint.This may further allow the integrated production simulator 310 toprovide a more accurate production forecast 313 and/or indication of thequantity of fluids 311 produced per phase over time.

As shown in FIG. 6, the integrated well-based production simulator 601comprises a system that includes: a capacitance-resistance (CR)simulator 602 configured to represent the flow of fluids in a productionreservoir, a well-bore simulator 603 configured to represent the flow ofone or more fluids in a wellbore, and a surface facility simulator 604configured to represent the flow of fluids through surface facilitiesincluding pipelines, production gathering facilities, separationfacilities and injection distribution facilities. Because the integratedwell-based production simulator 601 analyzes data from a CR simulator, awell-bore simulator and a surface facility simulator, the integratedsimulator 601 can provide a system-wide representation of fluid flow 605through the production reservoir, the wellbore and any one or more ofthe surface facilities.

At least in some embodiments, the fluids in the production reservoirinclude petroleum and various aqueous fluids. These fluids may flow to aseparator (e.g. 201 of FIG. 2) that is configured to isolate each fluidphase. For instance, the separator 201 may isolate the fluids into atleast three fluid phases including oil, gas and water. The fluid phasesmay be analyzed as being compressible when providing a system-widerepresentation of fluid flow through the production reservoir.Accounting for compressibility allows the integrated well basedproduction simulator 601 to provide a more accurate production systemsimulation. Along these lines, capillary forces and gravity forces mayalso be analyzed when providing a system-wide representation of fluidflow through the production reservoir 208.

The integrated well-based production simulator 601 may be designed toinclude different levels of control including the ability to controltubing head pressure, flow-line pressure located at the well headupstream and downstream of the choke, and downstream pressures offlow-lines, manifolds or separators. In this manner, a user (e.g. 305 ofFIG. 3) may use input 306 to control the tubing head pressure, flow-linepressure or downstream pressures. These may be controlled manually ormay be adjusted automatically upon a determination by the integratedproduction simulator that certain production system 320 components arecausing flow constraints.

In some cases, the well-based production simulator may be designed toimplement a flow function to determine fluid flow for the integratedwell-based production simulator system 310. The function may include ananalytical formula that expresses dependence on bottom-hole pressure todownstream pressure, dependence on flow rates and/or dependence onoperational parameters such as well-head choke size and artificial liftparameters. Still further, the integrated well-based productionsimulator may be designed to implement a numerical model of a flowfunction to determine fluid flow for the integrated well-basedproduction simulator system. As such, the flow function includes asurface facility simulator and/or a simulator of wellbore flow dynamics.At least in some cases, the flow function may include a table ofpre-computed solutions. Thus, as can be seen, the well-based productionsimulator may be designed to include multiple different features andfunctionality components in order to provide highly accurate indicationsof fluid flow through a production system 320.

Turning now to FIG. 5, a flowchart of a method 500 is illustrated forgenerating a production forecast for individual wells. The method 500will now be described with frequent reference to the components and dataelements of FIGS. 1, 2 and 3, respectively.

Method 500 includes accessing one or more operational parameters for awell (510). The operational parameters, as indicated above, may be usedto determine the quantity of fluids 311 produced per phase over time asa function of these operational field parameters. The simulatorgenerating module 309 of computer system 301 may use the operationalparameters to provide integrated well-based production simulator 310 bysolving the following system of equations (520):

p _(BH) =f _(p) _(c1) _(, . . . , p) _(cm) ^(facility)(p _(DS) , q _(o), q _(w)),   (Eq. 7)

where p_(c1), . . . , p_(cm) comprise m independent operationalparameters for the well,

q _(t) =j(p−p _(BH)),   (Eq. 5)

which links a total production rate to the reservoir pressure, and

$\begin{matrix}{{{\frac{S_{w}}{t} + {{S_{w}( {c_{w} + c_{f}} )}\frac{p}{t}} + \frac{q_{w} - i_{w}}{V}} = 0},} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$

which provides a mass conservation condition over one or more fluidcomponents written over the drainage volume of the well, where Vcomprises the drainage volume of the well. Once instantiated, thegenerated production simulator 310 may create a production forecast forthe well using the accessed operational parameters (530).

Within this production simulator 310, the bottom-hole pressure may betreated as an unknown, and as such, may depend on the architecture ofthe production system 320. This allows the production forecast to takeinto account multiple different factors, different designs, differentarchitectures, and different physical conditions present in the variouscomponents of the production system 320. The production forecast may begenerated quickly enough to drive operational decisions used to optimizethe exploitation of petroleum reservoirs. Moreover, the productionforecast 313 shows increased accuracy as it is based on actualproduction system information received from actual, working productionsystem components.

Accordingly, methods, systems and computer program products are providedwhich model a production system, taking into account each of thedifferent components of the production system. Moreover, methods,systems and computer program products are provided which generate aproduction forecast for individual wells.

The concepts and features described herein may be embodied in otherspecific forms without departing from their spirit or descriptivecharacteristics. The described embodiments are to be considered in allrespects only as illustrative and not restrictive. The scope of thedisclosure is, therefore, indicated by the appended claims rather thanby the foregoing description. All changes which come within the meaningand range of equivalency of the claims are to be embraced within theirscope.

We claim:
 1. At a computer system including at least one processor and amemory, a computer-implemented method for modeling a petroleumproduction system, the method comprising: accessing one or more portionsof first production system information from a capacitance-resistancemodel of the production system, the production system corresponding toat least one petroleum production reservoir; accessing one or moreportions of second production system information from at least one of awell-bore model, a flow line and a petroleum production facility;generating an integrated production simulator using both the first andsecond accessed production system information; and implementing theintegrated production simulator to determine the quantity of fluidsproduced per phase over time as a function of one or more operationalfield parameters corresponding to the petroleum production system byidentifying the flow rate for the production reservoir.
 2. The method ofclaim 1, wherein the integrated production simulator shows the flow ofpetroleum and aqueous fluids from the production reservoir through thepetroleum production facility.
 3. The method of claim 1, wherein the oneor more operational field parameters corresponding to the petroleumproduction system comprise well-head choke size and artificial liftparameters.
 4. The method of claim 1, wherein the determined quantity offluids produced per phase over time as a function of one or moreoperational field parameters corresponding to the petroleum productionsystem is further analyzed to determine whether additional operationsare to be initiated on the petroleum production system.
 5. The method ofclaim 4, further comprising, upon determining that additional operationsare to be initiated on the petroleum production system, analyzing thedetermined quantity of fluids produced per phase over time as a functionof one or more operational field parameters corresponding to thepetroleum production system to determine the degree to which theadditional operations are to be performed.
 6. The method of claim 1,wherein identifying the flow rate for the production reservoir comprisesidentifying a production rate for at least one of a plurality of fluidsin the petroleum production reservoir.
 7. The method of claim 1, whereinthe integrated production simulator accounts for fluid flow in both thewell-bore and the petroleum production facility.
 8. The method of claim7, wherein the integrated production simulator further accounts forfluid compressibility, capillary forces and gravitational forces.
 9. Themethod of claim 1, wherein bottom-hole pressure for the petroleumproduction system is identified as an unknown element.
 10. The method ofclaim 9, wherein the integrated production simulator is configured tomodel the bottom-hole's dependence on one or more components ofpetroleum production system architecture.
 11. An integrated well-basedproduction simulator system comprising: a capacitance-resistance (CR)simulator configured to represent the flow of one or more fluids in apetroleum production reservoir; a well-bore simulator configured torepresent the flow of one or more fluids in a well-bore; and a surfacefacility simulator configured to represent the flow of one or morefluids through at least one of the following surface facilities: one ormore pipelines, a production gathering facility, a separation facility,and an injection distribution facility; wherein the integratedwell-based production simulator is configured to provide a system-widerepresentation of fluid flow through the petroleum production reservoir,the wellbore and at least one of the one or more surface facilities. 12.The integrated well-based production simulator system of claim 11,wherein the one or more fluids comprise petroleum and one or moreaqueous fluids.
 13. The integrated well-based production simulatorsystem of claim 11, wherein the one or more fluids flow to a separatorthat is configured to isolate each fluid phase.
 14. The integratedwell-based production simulator system of claim 13, wherein theseparator isolates the fluids into at least three fluid phases (oil, gasand water).
 15. The integrated well-based production simulator system ofclaim 13, wherein the fluid phases are analyzed as being compressiblewhen providing a system-wide representation of fluid flow through thepetroleum production reservoir.
 16. The integrated well-based productionsimulator system of claim 11, wherein capillary forces and gravityforces are analyzed when providing a system-wide representation of fluidflow through the petroleum production reservoir.
 17. The integratedwell-based production simulator system of claim 11, wherein theintegrated well-based production simulator incorporates one or morecontrol mechanisms of the petroleum production reservoir including atleast one of well-head choke size and artificial lift parameters. 18.The integrated well-based production simulator system of claim 11,wherein the integrated well-based production simulator includesdifferent levels of control including one or more of the following:tubing head pressure, flow-line pressure located at the well headupstream and downstream of the choke, and downstream pressures offlow-lines, manifolds or separators.
 19. The integrated well-basedproduction simulator system of claim 11, wherein the integratedwell-based production simulator implements a flow function to determinefluid flow for the integrated well-based production simulator system,the function comprising an analytical formula that expresses dependenceon bottom-hole pressure to downstream pressure, flow rates and one ormore operational parameters.
 20. The integrated well-based productionsimulator system of claim 11, wherein the integrated well-basedproduction simulator implements a numerical model of a flow function todetermine fluid flow for the integrated well-based production simulatorsystem, such that the flow function comprises at least one of asimulator of wellbore flow dynamics and a surface facility simulator.21. The integrated well-based production simulator system of claim 11,wherein the integrated well-based production simulator implements a flowfunction to determine fluid flow for the integrated well-basedproduction simulator system, the flow function comprising a table ofpre-computed solutions.
 22. The integrated well-based productionsimulator system of claim 11, wherein the integrated well-basedproduction simulator system includes a computer system.
 23. At acomputer system including at least one processor and a memory, acomputer-implemented method for generating a production forecast forindividual production wells of a petroleum reservoir, the methodcomprising the following: accessing one or more operational parametersfor a production well; providing an integrated well-based productionsimulator by solving the following system of equations: (Eq. 7)p_(BH)=f_(p) _(c1) _(, . . . , p) _(cm) ^(facility)(p_(DS), q_(o),q_(w)), where p_(c1), . . . , p_(cm) comprise m independent operationalparameters for the production well; (Eq. 5) q_(t)=j(p−p_(BH)), whichlinks a total production rate to the reservoir pressure; and$\begin{matrix}{{{\frac{S_{w}}{t} + {{S_{w}( {c_{w} + c_{f}} )}\frac{p}{t}} + \frac{q_{w} - i_{w}}{V}} = 0},} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$ which provides a mass conservation condition over one ormore fluid components written over the drainage volume of the productionwell, where V comprises the drainage volume of the production well; andthe integrated well-based production simulator generating a productionforecast for the production well using the accessed operationalparameters.
 24. The method of claim 23, wherein the fluid componentscomprise water and oil.
 25. The method of claim 23, wherein theproduction forecast is generated without relying on assumptions aboutthe individual production well.